Starchy staples production shortfalls in Ghana: Technical inefficiency effects outweigh technological differences across ecologies

Starchy staples are a major source of livelihood support for farmers, traders, and processors who participate in these crops’ value chains, while also providing staple food to many people, especially the less affluent in society. Despite this position, the productivity figures of starchy staples are low. We use a unique data set and meta-frontier efficiency analysis to assess whether the production shortfalls of major starchy staple crops in Ghana could be attributed to technical inefficiency, technology gaps or both. Results show strong evidence of about 50% production shortfall for cassava, yam, cocoyam, and plantain. For cassava production, the Guinea Savannah zone has the most superior technology, with a technology gap ratio of 0.92, while yam production is more technically efficient in the Sudan Savannah zone, with a technical efficiency score of 0.67. Cocoyam production is more technically efficient (0.56) in the Transition zone, but yam is more technically efficiently produced in the Coastal Savannah zone of Ghana. These results show that production shortfall is more influenced by pure farmer technical inefficiencies (about 45%) rather than by technology gaps (about 20%) along ecological lines. Thus, the sector could benefit from improvements in farmer managerial skills and efficient use of existing technologies.


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The data underlying the results presented in the study are available from (include the name of the third party and Asia being the main hosts of these challenges. Since the primary source of food in these areas, 42 Africa especially, is domestic production, this, coupled with lessons from border closures due to 43 COVID-19, creates the urgent need to examine the pathways through which food is made available 44 and accessible locally to the vulnerable segment of the population. 45 In many parts of the developing world, smallholder farmers mostly organize the production of 46 staples. Several FAO reports on "The State of Food Security in the World" point to low agricultural 47 productivity as characterizing smallholder production systems. Productivity growth and resource 48 use efficiency are important causal pathways to increased food production and access for the poor 49 in these areas. Besides direct food availability, agro-industrial development rests so much on the 50 availability of raw materials to supply industrial needs. How could the industrial need be met if 51 what is available cannot satisfy direct consumption demand? Therefore, productivity growth in 52 these crops is crucial to foster rapid economic transformation in the developing world. 53 The production of cassava (Manihot Esculenta), cocoyam (Colocasia Esculenta), plantain (Musa 54 Sapientum) and yam (Dioscorea Alata) (henceforth called starchy staples) plays important roles 55 4 in the Ghanaian economy. Starchy staples are a major source of livelihood support for farmers, 56 traders, and processors who participate in these crops' value chains, while also providing staple 57 food to many people, especially the less affluent in society. Despite this position, the productivity 58 figures of starchy staples are low. There are non-ignorable productivity gaps between actual and 59 potential yields. In 2018 the national average yield of cassava, cocoyam, plantain, and yam were 60 all over 50% lower than their potential yields ( estimates showed that male farmers were marginally more efficient because of higher seeding and 181 fertilizer application rates, while the MTE and TGR showed that female-managed farms were 182 closer to the MSF than that of their male counterparts. However, by incorporating data from 183 multiple seasons, a recent study also using MSF showed that from 1987-2017, the overall crop 184 production gender gap in Ghana has reduced (Adaku, Tsiboe, and Clottey, 2022); thus, 185 highlighting the merits of incorporating data from multiple seasons. 186 Owusu (2016) investigated the impact of improved maize variety adoption on TE and productivity 187 from three agroecological zones using a Translog SFA combined with a matching estimator to 188 correct for selectivity bias. The results showed that adopters in the Semi-Deciduous Forest Zone 189 were between 25% to 36% more efficient than non-adopters, while in the Guinea Savannah Zone 190 adopters were between 15% to 26% more efficient than non-adopters. In the Transitional Zone, 191 adoption was found to diminish farmers' TE by 6-8%, which led to productivity losses of 12-19%. 192 The author attributed differences in TE and productivity effects to differences in levels of adoption 193 farmers contribute most to production shortfalls and how these evolve is not yet clear. Such 212 incomplete information might limit scientific progress, and serve as a barrier to societal 213 development, in the sense that policymakers may be inadequately or wrongly informed. Focusing 214 on starchy staples, this study addresses most of these limitations. 215

Study Area 217
Ghana covers a total land area of 24 million hectares of which 20.7% is considered arable, 40.7% 218 under forests, 11.8% under permanent crops, and 26.8% for other uses. As shown in Figure 1 The aridity of these ecologies increases from south to north. Farming systems across these 222 ecologies are highly heterogeneous. However, the highest yielding ecologies across all the starchy 223 staples are SFEZ and TEZ, and the low-yielding ones are SSEZ and RFEZ. Given their well-224 balanced annual rainfall and modest temperatures, SFEZ and TEZ have the optimal conditions to 225 grow starchy staples. Thus, it is not surprising that they also contain most of the cultivated lands 226 allocated to starchy staples as shown in Tables S1 and S2 (all tables, figures, and notes with a 227 leading S indicates supplemental materials in the online appendix) 228

Household Level Data Sources 229
The study uses data drawn from two sources: (1) the two waves of the Ghana Socioeconomic Panel The final sample used in this study was limited to starchy staple farmers drawn from the various 246 surveys, with yield (kg/ha) above the 5th and below the 90th percentile by survey, crop, and region. 247 The final sample, therefore, consisted of 15,402 farmers from 15,130 households, out of which 248 11,584, 3,468, 1,834, and 6,223 farmers cultivate cassava, yam, cocoyam, and plantain, 249 respectively. All of Ghana's ecologies are well represented in the final sample, and the dataset 250 captures 14 growing seasons. Nonetheless, due to sparse data, the RFEZ and SFEZ are combined 251 into a Forest Ecology (FEZ). Note S1 documents the definition of all the variables used in this 252 study. 253  Figure S1 (a and b). 261

Descriptive statistics 254
From Table 1 In terms of input utilization, planting material usage rate was estimated at 0.06 Mt/ha, with the 282 SSEZ having the highest and CSEZ having the lowest. On average, three household members 283 provided labor for starchy staple production, and this has significantly increased by about 0.45% 284 annually since 1987. Hired labor is used at a rate of about 11 man-days/ha, which is equivalent to 285 an annual increase of about 1.24% since 1987. The highest usage of hired labor is for farms in the 286 SSEZ, followed by TEZ, CSEZ, GSEZ, and FEZ, in that order. Farmers in the sample used about 287 8 liters of pesticides per hectare. Analyzing pesticide use across ecological zones and time shows 288 consistently low usage in the CSEZ relative to the SSEZ and FEZ for all crops. 289 Access to credit and extension (measured as dummies) depict factors that reflect the enabling 290 environment for production success. Access to credit and extension services are reported in the 291 literature to have diverse effects on technical inefficiency. The rate of access to credit or extension 292 is estimated at 19%; however, credit access is higher in FEZ and CSEZ than in the GSEZ and 293 SSEZ. Furthermore, Table 1 shows that the rate of access has significantly declined annually by 294 0.19% for credit but increased for extension by 0.75% from 1987 to 2017. 295

Theoretical framework 297
The prototypical output oriented SFA approach assumes that given their input set, DMUs utilizing 298 a homogeneous technology will put them at various points along the Stochastic Frontier (SF). 299 However, due to technical inefficiency and/or idiosyncratic shocks (i.e., downside production 300 risk), one may observe some DMUs below the SF. Furthermore, some DMUs may also be 301 observed above the SF solely due to upside production risk. The SFA is formulated as: 302 where, is the output level when inputs are used with the technology ( ). The deviations 304 from the SF due to technical inefficiency and production risk are represented by and , 305 respectively. Consequently, the assumptions made on the distribution of the deviations ( and ) 306 underpin the estimation of Equation (1). Generally, is assumed to follow a normal distribution 307 with zero mean and variance 2 [~(0, 2 )]. However, due to its negative skewness, is 308 assumed to follow either half-normal, exponential, truncated, or gamma distributions ( where is strictly positive, implying that ( ) ≤ ( ). Consequently, the ratio of group j's 327 stochastic frontier to the MSF is the TGR, represented as 328 The TGR depends on the accessibility and adoption level of the available MSF which in turn 330 depends on DMU-specific circumstances. The MTE represents each DMU's technical efficiency 331 with respect to the meta frontier production technology. MTE can be decomposed into the TE 332 (which is the efficiency measured with respect to the zone-specific frontier) and the TGR (which 333 is the difference between the best available technology and the technology set adopted). 334 Accordingly, each DMU's MTE is given by equation (5)  Tsiboe, 2021) production function. However, due to its relative flexibility, this study implements 340 the MSF assuming that (•) is Translog. Nonetheless, since Cobb-Douglas is nested within the 341 Translog, the former was tested after estimation and was soundly rejected at p<0.01 for all models 342 (see Table 2). Thus, the Translog specification is appropriate for the data, and is represented as 343 where, is total production (Mt) for the i th farmer in ecology j at time t. Each represents 346 the k th input (i.e., land, planting material, family and hired labor, and pesticides) used by the i th 347 farmer and a trend variable. As Van Nguyen et al. (2021) report, outcomes from the SFA, 348 especially with efficiency estimates, are severely affected by the distribution assumptions. 349 Therefore, we considered all possible distributions but eventually had to settle on a half-normal 350 distribution (i.e., The parameters of the ecology-and meta-frontiers were estimated via maximum likelihood, using 355 the "frontier" command in Stata 15. Furthermore, estimation variables were standardized by their 356 sample means by survey and crop. Given the parameters, input elasticities were estimated as the 357 first derivative of the frontiers with respect to that input, evaluated at their means. The production 358 returns to scale (RTS) were then estimated as the sum of the input elasticities. The standard errors 359 for the elasticities were estimated via the delta method. Ecology-specific technical TE, TGR, and 360 MTE were estimated using equations (2), (4), and (5), respectively. Estimates were summarized 361 by ecology and season. 362

Results and Discussions 363
The core objective of this paper is to understand whether production shortfalls existing among the 364 starchy staples in Ghana are driven by technological gap, inefficiency, or both. Thus, the 365 discussion in this section seeks to accomplish that purpose. In the interest of space, we only present 366 the diagnostic test of the empirical model, estimates of elasticities, and estimates of TE, TGR, and 367 MTE in the main text in Tables 2, 3, and 4, respectively. The paper presents the estimates of the 368 production functions (Tables S3-S6) and drivers of technical inefficiency (Table S7) in the online  369 appendix. Discussions on the drivers of technical inefficiency and technology gaps are also 370 presented in Note S3 in the online appendix. Trends of TE, TGR, and MTE are also presented in 371 Figure 2 and Table S8. 372 The diagnostics on the negative skewed error needed for the justification of SFA shown in Table  373 2 confirmed the appropriateness of the empirical model. Further, test results on the similarity of 374 production frontiers across ecologies support the fact that starchy staple farmers are operating 375 under heterogeneous technologies in the various ecologies. This justifies further inquiry into 376 whether technology gaps across ecologies or resource use inefficiencies among farmers contribute 377 most to production shortfalls. The major learning points include the wide range of the estimated 378 values which suggest that considerable amounts of the observed crop output variation for the 379 ecology-frontiers [meta-frontier] could be attributed to inefficient use of inputs [technological 380 gaps]. The range of for the meta-frontier also suggests that the observed variation in output, 381 given the ecology-frontiers, could not be attributed to idiosyncrasies. Also, the SFA model chi-382 squared test statistics indicate that the models are all significant. Specific discussion of relevant 383 diagnostics is presented in Note S2. 384

Elasticities and Meta-Frontier Results of Ghanaian Starchy Staples Production 385
Based on production elasticities for cassava in Table 3, land is the most operationally significant 386 input contributing the highest to output across all ecological zones. The contribution of family 387 labor, hired labor, planting material, and pesticide use follow in that order across all ecological 388 zones. The returns to scale (RTS) estimates for all ecologies and the meta-frontier confirm 389 decreasing returns to scale (DRS). 390 Like cassava, land contributes the highest returns to yam production in all ecological zones. Yam 391 is a very dominant cash crop enterprise. Due to the laborious nature of the enterprise, farmers often 392 employ hired labor as much as they rely on family labor. The labor activities in yam production 393 are often specialized (e.g., making mounds) and this limits issues of moral hazard from hired labor. 394 Hence, it is not too surprising that family labor (for SSEZ and FEZ), as well as hired labor ( Table S8. These results show the temporal and spatial dynamics of starchy staple-417 specific technology level and technical efficiency. 418

Cassava Production 419
In the data, four main ecological zones were significantly involved in cassava production, which 420 are the GSEZ, TEZ, FEZ, and CSEZ. The TGR ranges from zero to one: values closer to one 421 indicate the use of technology like the meta-frontier technology. In other words, the technology 422 gap is the difference between the best technology available in the production of a particular crop 423 and the technology set adopted by a farmer. The choice of the adopted technology is determined 424 by environmental and socio-economic factors. The mean TGR for cassava, estimated at 0.91, 425 indicates that the technological gap averaged over all ecological zones is 9%. An important caveat about these TE scores is that they do not rank the farms based on the 438 cassava meta frontier, so we discuss the MTE results next. 439 After accounting for ecology-specific production technologies, the mean MTE across the entire 440 sample is 0.57. Specifically, the most technically efficient cassava farmers relative to the 441 metafrontier are those in the GSEZ with an MTE of 0.61. Table 4 shows that the GSEZ farmers 442 are followed by their peers in FEZ (0.57), CSEZ (0.56), and then TEZ (0.55). such as credit to purchase fertilizer, among others) to produce optimal yields. However, such 449 conditions often are not the focus of research (P. P. Acheampong, Owusu, and Nurah, 2018) or 450 government policies after introducing improved seeds to farmers. Overall, the high TGR and high 451 resource wastage (farmers in the most efficient ecological zone can reduce input use by 36% and 452 achieve the same output) show that cassava MTE could be enhanced by improving TE. 453

Yam Production 454
Yam is significantly cultivated in four main ecological zones, which are the SSEZ, GSEZ, TEZ, 455 and FEZ. Mean TGR for yam, estimated at 0.93, is higher than that of cassava. Thus, on average 456 the ecological technology gap for yam is only about 7%. Figure 2, panel a2 shows that the TGR 457 for yam has remained relatively constant at an average of 0.93 over the study period. At the ecology 458 22 level, the highest TGR is estimated for SSEZ (0.98). Table 4 shows that SSEZ is followed by 459 GSEZ (0.98), TEZ (0.93), and then FEZ (0.84). 460 Ecology-specific TE for yam is estimated at a mean of 0.55 and has remained relatively constant 461 over the study period (Figure 2, panel b2). Yam TE follows similar patterns as the TGR, the only 462 difference being that FEZ (0.51) is higher than TEZ (0.49) and the highest found in SSEZ (0.64) 463 as reported in Table 4 Accounting for ecology-specific production technologies shows that the mean yam MTE is 0.51. 477 Like the TGR and TE, the MTE for yam is also constant across the study period but improves from 478 Southern to Northern Ghana. Specifically, the most technically efficient yam farmers relative to 479 the industrial production frontier are those in the SSEZ with an MTE of 0.66. Table 4

Cocoyam Production 495
Cocoyam production dominates only in two agroecological zones (TEZ and FEZ). This is hardly 496 surprising since these areas are also famous for their high cocoa production. Traditionally, 497 cocoyam was grown as a complement to provide shade for young cocoa seedlings (P. Acheampong 498 et al., 2014). Even though cocoyam production has since expanded beyond this basic use, its 499 proliferation in the country has been largely restricted to specific areas in southern Ghana. Coupled 500 with the fact that the cocoyam was originally introduced in the forest belt, the dominance of 501 24 cocoyam production in the TEZ and FEZ has been mainly attributed to the wide vegetation cover 502 that the forest belt provides as well as the well-distributed rainfall amounts (Aidoo et al., 2019). 503 This assertion is given credence by the fact that the FEZ (Semi-Deciduous and Rain Forests) and 504 TEZ recorded the highest average rainfall estimates of all ecological zones in this study (Table  505   S1). 506 Mean TGR for cocoyam is lower than the estimates for cassava and yam. The estimated cocoyam 507 TGR of 0.82 indicates that the ecological technology gap is about 18%. Figure 2, panel a3 shows 508 that the TGR for cocoyam has remained at a higher value of 0.80 over the study period. This 509 reflects the same dynamics in the yam TGR but at a lower magnitude. At the ecology level, the 510 TGR for cocoyam is higher in the FEZ (0.84) than in the TEZ (0.80). 511 As indicated, TGR is dependent on the accessibility and adoption level of frontier-enhancing 512 technologies. For cocoyam production in Ghana, P. P. Acheampong, Owusu, and Nurah (2018) 513 examined the rates of adopting improved technologies, the determinants of adoption, and the 514 benefits of adopting cocoyam technologies to farmers. The results of their analysis partly resonate 515 with ours because they drew their sample from similar ecological zones. Among others, P. P. 516 Acheampong, Owusu, and Nurah (2018) found that only about 22% of farmers adopted improved 517 planting material varieties although more than 60% were aware of their availability. Similarly, low 518 rates of adoption were realized for agronomic practices such as the application of manure, 519 inorganic fertilizer, cover cropping, minimum tillage, and row planting. However, there were high 520 adoption levels for techniques such as appropriate planting distance, timely weed control, and 521 weedicide application. With regards to the determinants of adoption, they found that experienced 522 farmers and farmers whose farm plots were far removed from their homestead had lower 523 probabilities of adopting all technologies. The opposite was observed for farmers that belonged to 524 25 farmers' associations primarily because they were more aware of the benefits of new technologies 525 through higher social integration. The study reported significant gender and ecological variations 526 although unlike in this study, they found higher adoption among farmers in the TEZ than those in 527 the FEZ. 528 Ecology-specific TE for yam is estimated at a mean of 0.52 across the entire sample. Unlike the 529 TGR, the cocoyam TE has been declining at a rate of about 5.2% from 1987 to 2017 (Figure 2, 530 panel b3). Contrary to the TGR, cocoyam TE is higher in TEZ (0.57) than in the FEZ (0.48) as 531 depicted in Table 4. 532 The dynamics in TGR and TE culminate in an estimated mean MTE of 0.41 for cocoyam. This 533 follows the temporal trend exhibited by TE but improves from Southern to Northern Ghana. Unlike 534 cassava and yam, the overall results for cocoyam show that production could be enhanced by 535 improving both TGR and TE. The estimated RTS parameter for cocoyam (0.45) is the lowest for 536 all the starchy staples considered in this study. Just like for cassava and yam, the implications are 537 that extensification of production (for example, through area expansion) is not a feasible entry 538 point to change the status quo. 539

Plantain Production 540
Like yam and cocoyam, plantain has traditionally been integrated as a complementary crop to 541 cocoa. Its broad leaves and succulent stems provide ecosystems services to young cocoa seedlings. 542 Hence, our finding that plantain cultivation takes place in three ecological zones, namely, the TEZ, 543 FEZ, and CSEZ is to be expected as these zones describe the "cocoa belt" of Ghana. Apart from 544 providing shade for cocoa seedlings, smallholder farm households in Ghana secure their food by 545 integrating plantain into tree crop plantations to offset the income gap between plantation 546 establishment and commercial yields. 547 26 Plantain is integral to the food security and poverty alleviation efforts of Ghana. Its utilization in 548 the diets of the producing areas is varied and has been increasing in recent times (MoFA, 2019). 549 Results from Table 3 show that land has the highest output elasticity across all ecological zones 550 and is followed by family labor, hired labor, pesticide, and then planting materials. Like the other 551 starchy staples, plantain production exhibits DRS in all ecological zones and nationally. The 552 estimated RTS of 0.63 implies that a 1% increase in all inputs will yield about 0.63% increase in 553 plantain output. Practically, this means the cost-saving or profit returns of increasing all the factors 554 of plantain production have been attained, given the present technology. 555 Mean TGR for plantain (0.91) is lower than that of cassava and yam but higher than cocoyam. 556 This indicates a plantain technology gap of about 9%. district attains less than 50% of the estimated potential yield. 571

Conclusions and Recommendations 572
Improvements in crop productivity by closing production gaps in rural economies are generally 573 touted as an important pathway to achieving three main developmental goals, namely food 574 security, reduced poverty, and sustainable resource utilization. Yet, many farmers unwittingly 575 waste significant productive resources through production and technological inefficiencies which 576 engender poverty and food insecurity. Changing the status quo and fostering rapid economic 577 transformation would require sustained improvements in agricultural productivity. In this study, 578 we take advantage of a large Ghanaian dataset that spans over three decades and examine the 579 spatiotemporal trends of production gaps of one of the most important staple crop categories, the 580 starchy staples. Unlike similar studies thus far, and in addition to the unique dataset used, the 581 method adopted accounts for technological heterogeneity across the main ecological zones in 582 Ghana where these starchy staple crops are cultivated. Using the stochastic meta-frontier approach, 583 technical efficiency, meta-technical efficiency, and technology gap ratios are measured across 584 crops and ecological zones. The main aim of doing this analysis is to provide a broad understanding 585 of what the starchy staples industry looks like, the major growth points, and the limitations that 586 need critical policy attention. 587 The key findings suggest that there are significant shortfalls in starchy staples production among 588 agroecological zones, which could be more attributed to technical efficiencies and less to 589 technology gaps. Stunningly, the results show that the current set of production technologies used 590 by starchy staple farmers in Ghana across ecologies are quite similar as there exists an ecological 591 technology gap of less than 13% across all four crops. On the other hand, there exist significant 592 28 production shortfalls that could be more attributed to technical inefficiencies. Respectively, about 593 37, 45, 48, and 46% of cassava, yam, cocoyam, and plantain production is lost due solely to 594 technical inefficiencies and these have been relatively constant or at worst increased from 1987 to 595 2017. These dynamics suggest that over time, limited attention might have been given to 596 interventions that inure to improve the efficiency of resource use among starchy staple farmers. 597 With the large production shortfalls attributable largely to technical inefficiency, one implication 598 of our results is that the starchy staples crop sector could benefit from improvements in conditions 599 that enhance farmer technical efficiencies, such as providing starchy staple farmers more access  Table 1. Summary Statistics of Starchy Staple Producing Farmers in Ghana (1987Ghana ( -2017 Ghana (1987Ghana ( -2017